Generalization of human grasping for multi-fingered robot hands

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Generalization of human grasping for multi-fingered robot hands

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Ben Amor, Heni / Kroemer, Oliver / Hillenbrand, Ulrich / Neumann, Gerhard / Peters, Jan

Multi-fingered robot grasping is a challenging problem that is difficult to tackle using hand-coded programs. In this paper we present an imitation learning approach for learning and generalizing grasping skills based on human demonstrations. To this end, we split the task of synthesizing a grasping motion into three parts: (1) learning efficient grasp representations from human demonstrations, (2) warping contact points onto new objects, and (3) optimizing and executing the reach-and-grasp movements. We learn low-dimensional latent grasp spaces for different grasp types, which form the basis for a novel extension to dynamic motor primitives. These latent-space dynamic motor primitives are used to synthesize entire reach-and-grasp movements. We evaluated our method on a real humanoid robot. The results of the experiment demonstrate the robustness and versatility of our approach.

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Titel :

Generalization of human grasping for multi-fingered robot hands

Beteiligte:

Ben Amor, Heni (Autor:in) / Kroemer, Oliver (Autor:in) / Hillenbrand, Ulrich (Autor:in) / Neumann, Gerhard (Autor:in) / Peters, Jan (Autor:in)

Kongress:

Erscheinungsdatum :

2012

Medientyp :

Aufsatz (Konferenz)

Format :

Elektronische Ressource

Sprache :

Englisch

Schlagwörter :

robotic grasping , generalization , learning

Generalization of human grasping for multi-fingered robot hands

Generalization of human grasping for multi-fingered robot hands

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